Human movement simulation tools are used for ergonomic analysis of workplaces, products, training and service operations, as well as in the entertainment, industry. The process of accurately representing human movement is tedious, time-consuming, and requires skilled operators adept at manipulating complex 3D kinematic systems at the joint level. Efforts to model human movement using empirical observation of actual people performing tasks is referred to as motion capture technology. Subsequent statistical modeling of these movement data are limited by the form of the data. Both joint angle data over time and landmark data over time datasets are available. However, joint angle data may not be applied to arbitrary skeletal configurations because the angle definitions are dependent on the skeletal configuration. Landmark data require constraint solutions, in which the kinematic human “skeleton” is best fit to the landmark data using mathematical optimization methods, which are slow and inconsistent. Another limitation of the current approach is that these empirical data tend to reflect the experimental conditions under which they were experimentally observed in the lab. For example, always beginning a movement from a “neutral starting posture.” In most simulations, however, the ending posture of the previous motion defines the starting posture of the next, so movements from arbitrary start postures are required. Collecting data and developing empirical models for the almost infinite number of tasks and loading conditions of which humans are capable are remote.
Another human movement modeling method utilizes key frame locations, such as in the robotics field. In this method, simple posture transition interpolators drive all joints such that they start moving and end at the same time. This results in a robotic looking motion, which looks unrealistic.